In 2023, the International Conference on Machine Learning (ICML) required authors with multiple submissions to rank their submissions based on perceived quality. In this paper, we aim to employ these author-specified rankings to enhance peer review in machine learning and artificial intelligence conferences by extending the Isotonic Mechanism (Su, 2021, 2022) to exponential family distributions. This mechanism generates adjusted scores closely align with the original scores while adhering to author-specified rankings. Despite its applicability to a broad spectrum of exponential family distributions, this mechanism's implementation does not necessitate knowledge of the specific distribution form. We demonstrate that an author is incentivized to provide accurate rankings when her utility takes the form of a convex additive function of the adjusted review scores. For a certain subclass of exponential family distributions, we prove that the author reports truthfully only if the question involves only pairwise comparisons between her submissions, thus indicating the optimality of ranking in truthful information elicitation. Lastly, we show that the adjusted scores improve dramatically the accuracy of the original scores and achieve nearly minimax optimality for estimating the true scores with statistical consistecy when true scores have bounded total variation.

翻译：2023年，国际机器学习大会要求有多篇投稿的作者根据感知质量对其投稿进行排序。本文旨在通过将保序机制扩展到指数族分布，利用这些作者指定的排序来改进机器学习和人工智能领域的同行评审过程。该机制生成的调整分数在遵循作者指定排序的同时与原始分数高度一致。尽管该机制适用于广泛的指数族分布，但其实现无需了解具体的分布形式。我们证明，当作者的效用函数形式为调整评审分数的凸可加函数时，作者有动机提供准确的排序。对于指数族分布的某个子类，我们证明只有当问题涉及作者投稿之间的成对比较时，作者才会真实报告，从而表明排序在真实信息引导中的最优性。最后，我们证明调整分数能显著提升原始分数的准确性，并在真实分数具有有界全变差时，以统计一致性逼近真实分数估计的极小极大最优性。